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CSC-40102 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2022/23 Last Updated: 14 February 2023

CSC-40102 - AI and Data Science Ambassador Scheme
Coordinator: Edward De Quincey Tel: +44 1782 7 34090
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2022/23

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2022/23


Aims
This module aims to promote areas within AI and Data Science and challenge the negative stereotypes sometimes associated with them.
Level 7 students will be provided with an opportunity to volunteer with a local organisation (or combination of organisations) such as Schools, Colleges, Universities, Charities, Small Businesses etc in order to gain confidence in applying and communicating their subject as well as developing strong organisational and interpersonal skills that will be of benefit to them in employment and life. It will also enable them to understand how to address the needs of people and organisations and devise and develop suitable resources, materials, software etc. appropriate to the organisation they are working with.
This module also provides the skills and opportunity to allow the student to act as an enthusiastic role model for AI and Data Science and offers the student a positive experience of working with people in a host organisation(s).

Intended Learning Outcomes

propose a plan of work with defined goals to meet the needs of an organisation: 1
apply appropriate organisational, prioritisation and negotiating skills to ensure achievement of defined goals: 2
select and demonstrate relevant AI and Data Science approaches to meet defined goals: 1,2
assess their impact as a role model for AI and Data Science on an organisation: evaluate their experience and communicate this professionally to peers and staff in an appropriate format:

Study hours

4 hours of training.
2 hours of meetings/support from a supervisor at the University.
3 hours attendance at end of module mini-conference.
20 hours independent study (assessment preparation).
121 hours placement (comprised of meetings, events, in-situ work, preparation, self-study etc.)

School Rules

Interview by module co-ordinator where the student's motivation and suitability for the module will be determined.
Successful completion of a Disclosure and Barring Service (DBS) check (if required).
Secured place in a host organisation(s) prior to the start of semester 2 (this will be primarily be the student's responsibility but we will support with contact details of appropriate organisations and make introductions where necessary).

Description of Module Assessment

1: Marketing Plan weighted 10%
Plan of Work
Students will propose a plan of work in collaboration with the host organisation(s) and University supervisor. It will contain a clear definition of goals, timescales, how the required number of hours will be completed and identification of risks and mitigations. This plan will be iterative as the module progresses with the final version being submitted. The plan of work is marked by the University module staff. The approximate page limit for this is 4 pages (including a Gantt chart and table of risks).

2: Workbook weighted 40%
Logbook
Students will be expected to keep a weekly online logbook, to help them keep an accurate record of what they do in their placement. This should include a clear log of goals met and which skills/approaches have been applied and developed as they progressed through the scheme. Clear evidence of how students have met the required hours should be included. The logbook is marked by the University module staff, based in part on the host organisations confirmation that the logbook is an accurate reflection of what the student did and a standard observation form completed by the host organisation. Each logbook entry (equivalent to a week's activity) should be a maximum of 2 pages.

3: Presentation weighted 50%
End of module presentation
Students will be required to produce a presentation for an end of module mini-conference, outlining the goals they have achieved, the relevant AI and Data Science approaches that they have demonstrated, the skills they have used and developed, an assessment of their impact as a Role Model and an overall evaluation of their experience. Students may choose to either give a "live" 10-minute oral presentation or produce a pre-recorded 10-minute video.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 14 February 2023